1 Emulating AQM from End Hosts Presenters: Syed Zaidi Ivor Rodrigues.

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Emulating AQM from End Hosts

Presenters:Syed Zaidi

Ivor Rodrigues

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Introduction

Congestion control at the End Host

Treating the Network as a Black Box

Main indicator Round Trip Time

Probabilistic Early Response TCP (PERT)

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Motivation

Implementing AQM at the Router is not easy. Current techniques depend on Packet loss to detect

congestion. Easier to modify TCP stack at the End Host. Can work any AQM mechanism at the router.

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Challenges

RTT based estimation have been characterized to be inaccurate.

Hard to measure Queuing Delays when they are small compared to the RTT.

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Accuracy of End-host Based Congestion Estimation

Previous studies looked at the relation between increase in RTT and packet loss for a single stream.

Results 1. Losses are preceded by increase in RTT in very

few cases.

2. Responding to a false prediction results in severe loss in performance.

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4 is false negative and 5 is false positive

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Previous studies claim transition 5 happens more then transition 2

Limitation of previous studies is to look at the relation between higher RTT in packet loss for a single flow

Packet loss should be looked at the router not for a single flow

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Ns-2 simulationTwo routers connected to a100 Mps link with end nodes having 500 Mbps

link, different combination of long term and short term flows. The reference flows have RTT of 60ms which is equal to 12000Km.

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Different Congestions Predictors

Efficiency of Packet loss prediction(Number of 2 transitions)/(2 transitions +5 transitions)

False Positives(Number of 5 transitions)/(2 transitions +5 transitions)

False Negatives(Number of 4 transitions)/(2 transitions +4 transitions)

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Previous WorkIn 1989 first paper was published proposing to enhance

TCP with delay-based congestion avoidance.

TRI-S: Throughput is used to detect congestion instead of delay DUAL: Current RTT is compared with Average of Minimum and

Maximum RTT Vegas: Achieved throughput is compared to expected

throughput based on minimum Observed RTT. CIM: Moving Average of small number of RTT samples is

compared with moving average of large number of RTT samples CARD: Congestion Avoidance using RTT Delay

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Improving Congestion Prediction

*Vegas, Card, TRI-S, and dual obtain RTT samples once per RTT.

Smoothed RTT Exponential Weighted Moving Average

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Improving Congestion Prediction

We improve accuracy by more frequent sampling and history information

End-host congestion prediction is not perfect, thus we need mechanisms to counter this inaccuracy.

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Response to Congestion PredictionHow do we reduce the impact of FALSE Positives?

Keeping the amount of Response small.

Respond Probabilistically.

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Not much Loss in throughput

Maintains High link Utilization

Buildup of the bottleneck queue “may not be cleared out” quickly.

VEGAS

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No Loss of throughput Maintains High link

Utilization Buildup of the

bottleneck queue “may not be cleared out” quickly.

VEGAS

This causes a tradeoff in the fairness properties of TCP to maintain high link utilization

Vegas uses “additive decrease” for early congestion response

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Utilization Buildup of the bottleneck

queue “may not be cleared out” quickly.

VEGAS

This causes a tradeoff in the fairness properties of TCP to maintain high link utilization

AI/AD for these transitions will result in compromising the fairness properties of the protocol.

Vegas uses “additive decrease” for early congestion response

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Utilization Buildup of the

bottleneck queue “may not be cleared out” quickly.

VEGAS

Compared to the flow starting earlier, flows that start late may have a different idea of the Minimum RTT on the path.

This gives an unfair advantage to flows starting later, giving them more share of the Bandwidth.RTT= Propagation Delay

+ Queuing Delay

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When the probability of false positives is high, the probability of response to an early congestion signal should be low

High Probability of False Positives Low Response!

Low Probability of False Positives High response!

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Designing the Probabilistic ResponseFalse positives occur…

False Positives occur when the queue length is smaller.

False positives occur when the queue length is less than 50% of the total queue size.

srtt0.99 is the signal congestion predictor

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Designing the Probabilistic Responsewhat should be my response function?

Response should be

Small for low queue size

Response should large for large queue size.

srtt0.99 is the signal congestion predictor

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Designing the Probabilistic Responsewhat should be my response function?

Thus we emulate the probabilistic response function of RED.

Thus

P - probabilistic

E - early

R - response

T - TCP

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PERT

Tmin = Minimum Threshold =P+ 5ms=5ms

Tmax = Maximum Threshold=P+10ms=10ms

pmax =maximum probablity of response=.05 P= propagation delay= ??= 0!!!

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Probabilistic Response Curve used by PERT

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Is it necessary to have a 50% reduction in the congestion window in case of early response??

Routers are commonly set to the Bandwidth Delay Product of the Link since the TCP flow reduces its window by 50%

If B is the buffer size and f is the window reduction factor, the relationship between them is given by

Since the flows respond before the bottleneck queue is full, a large multiplicative decrease can result in lower link utilization but reducing the amount of response make it hard to empty the buffer, leading to unfairness.

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Experimental Evaluation Impact of Bottleneck link Bandwidth

Setup: Single bottleneck with bottle neck bandwidth between 1 Mbps to 1Gbps, RTT from 10ms to 1s. Simulations run for 400s. Results measured between stable period. RTT set to 60ms.

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Experimental Evaluation

Impact of Round Trip DelaysThe bottleneck link bandwidth is 150 Mbps and number of flows is 50. The end-to-end delay is varied from

10ms to 1s.

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Experimental Evaluation

Impact of Varying the Number of Long-term Flows.

Link bandwidth set to 500 Mbps, end to end delay set to 60ms.

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Bottle Neck Link b/w -150Mbps

End-End Delay - 60ms

Long term Flows – 50

Short Term varying from 10 to 1000

Bottle Neck Link b/w -150Mbps

End-End Delay – n * 12

1<n<10

Short Term - 100

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Multiple Bottlenecks

Bottleneck link bandwidth –150Mbps; Delay - 5ms; Link capacity – 1 Gbps; Delay – 5ms

Response to sudden changes in responsive traffic:

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Modeling of PERT

Forward propagation delay:

C – link capacity ; q(t) – queue size at time t ;

Note: Queuing Delay is perceived before R(t)

The Window Dynamics of PERT:

( A )

( 2 )

( 3 )

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Modeling of PERTNote: PERT makes its decision at the end host and not the router.

Incoming rate y(t) =>

( 5 )

( 6 )

( 4 )

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Modeling of PERTBy equation (A)

( 7 )

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Simulations Stability

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Emulating PI

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Discussion

Impact of Reverse traffic

Co-existence with Non-Proactive Flows

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Conclusion

Congestion prediction at end host is more accurate than characterized by previous studies, but requires further research to improve the accuracy of end host delay-based predictors.

PERT emulates the behavior of AQM in the congestion response function

Benefits are similar to ECN Its link utilization is similar to router –based schemes PERT is flexible, in the sense that other AQM

schemes can be emulated.

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Few of Our Observations

The authors have put a good deal of effort, but is its as simple and eye-catching if we implemented on any kind of network in real time?

What modifications have to now be made at the end host, such as additional hardware/software and cost??

Is it compatible with other versions of TCP? Will this implementation give an advantage to other

connections less/least proactive connections or misbehaving connections to take advantage of my readiness to lessen the job a router has to perform?

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Questions

1 Emulating AQM from End Hosts Presenters: Syed Zaidi Ivor Rodrigues.

Documents

rtt delay slide

vegas slide

current rtt

higher rtt

challenges rtt

false positive slide

accuracy of end

congestion estimation